Overview

Dataset statistics

Number of variables31
Number of observations601866
Missing cells2866841
Missing cells (%)15.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory142.3 MiB
Average record size in memory248.0 B

Variable types

Numeric20
Categorical5
Text6

Alerts

CANCELLED is highly imbalanced (83.5%)Imbalance
DIVERTED is highly imbalanced (96.5%)Imbalance
DEP_TIME has 13965 (2.3%) missing valuesMissing
DEP_DELAY has 13979 (2.3%) missing valuesMissing
TAXI_OUT has 14563 (2.4%) missing valuesMissing
TAXI_IN has 14764 (2.5%) missing valuesMissing
ARR_TIME has 14764 (2.5%) missing valuesMissing
ARR_DELAY has 16808 (2.8%) missing valuesMissing
CANCELLATION_CODE has 587260 (97.6%) missing valuesMissing
AIR_TIME has 16808 (2.8%) missing valuesMissing
CARRIER_DELAY has 434786 (72.2%) missing valuesMissing
WEATHER_DELAY has 434786 (72.2%) missing valuesMissing
NAS_DELAY has 434786 (72.2%) missing valuesMissing
SECURITY_DELAY has 434786 (72.2%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 434786 (72.2%) missing valuesMissing
SECURITY_DELAY is highly skewed (γ1 = 86.50351659)Skewed
DEP_DELAY has 27047 (4.5%) zerosZeros
ARR_DELAY has 10416 (1.7%) zerosZeros
CARRIER_DELAY has 70641 (11.7%) zerosZeros
WEATHER_DELAY has 155236 (25.8%) zerosZeros
NAS_DELAY has 90483 (15.0%) zerosZeros
SECURITY_DELAY has 166287 (27.6%) zerosZeros
LATE_AIRCRAFT_DELAY has 72648 (12.1%) zerosZeros

Reproduction

Analysis started2024-03-30 06:04:19.192239
Analysis finished2024-03-30 06:07:14.050459
Duration2 minutes and 54.86 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0514733
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:14.228229image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0744099
Coefficient of variation (CV)0.51201373
Kurtosis-1.3152161
Mean4.0514733
Median Absolute Deviation (MAD)2
Skewness-0.05710063
Sum2438444
Variance4.3031766
MonotonicityIncreasing
2024-03-30T03:07:14.461120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 99253
16.5%
7 97766
16.2%
6 90575
15.0%
4 80967
13.5%
5 80923
13.4%
3 78132
13.0%
2 74250
12.3%
ValueCountFrequency (%)
1 99253
16.5%
2 74250
12.3%
3 78132
13.0%
4 80967
13.5%
5 80923
13.4%
6 90575
15.0%
7 97766
16.2%
ValueCountFrequency (%)
7 97766
16.2%
6 90575
15.0%
5 80923
13.4%
4 80967
13.5%
3 78132
13.0%
2 74250
12.3%
1 99253
16.5%

FL_DATE
Categorical

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
7/27/2023 12:00:00 AM
 
20464
7/20/2023 12:00:00 AM
 
20461
7/21/2023 12:00:00 AM
 
20459
7/28/2023 12:00:00 AM
 
20444
7/24/2023 12:00:00 AM
 
20443
Other values (26)
499595 

Length

Max length21
Median length21
Mean length20.723372
Min length20

Characters and Unicode

Total characters12472693
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row7/3/2023 12:00:00 AM
2nd row7/3/2023 12:00:00 AM
3rd row7/3/2023 12:00:00 AM
4th row7/3/2023 12:00:00 AM
5th row7/3/2023 12:00:00 AM

Common Values

ValueCountFrequency (%)
7/27/2023 12:00:00 AM 20464
 
3.4%
7/20/2023 12:00:00 AM 20461
 
3.4%
7/21/2023 12:00:00 AM 20459
 
3.4%
7/28/2023 12:00:00 AM 20444
 
3.4%
7/24/2023 12:00:00 AM 20443
 
3.4%
7/17/2023 12:00:00 AM 20439
 
3.4%
7/13/2023 12:00:00 AM 20426
 
3.4%
7/14/2023 12:00:00 AM 20422
 
3.4%
7/31/2023 12:00:00 AM 20420
 
3.4%
7/10/2023 12:00:00 AM 20282
 
3.4%
Other values (21) 397606
66.1%

Length

2024-03-30T03:07:14.747112image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
12:00:00 601866
33.3%
am 601866
33.3%
7/27/2023 20464
 
1.1%
7/20/2023 20461
 
1.1%
7/21/2023 20459
 
1.1%
7/28/2023 20444
 
1.1%
7/24/2023 20443
 
1.1%
7/17/2023 20439
 
1.1%
7/13/2023 20426
 
1.1%
7/14/2023 20422
 
1.1%
Other values (23) 438308
24.3%

Most occurring characters

ValueCountFrequency (%)
0 3069956
24.6%
2 2059635
16.5%
/ 1203732
 
9.7%
1203732
 
9.7%
: 1203732
 
9.7%
1 877046
 
7.0%
3 700158
 
5.6%
7 662367
 
5.3%
A 601866
 
4.8%
M 601866
 
4.8%
Other values (5) 288603
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12472693
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3069956
24.6%
2 2059635
16.5%
/ 1203732
 
9.7%
1203732
 
9.7%
: 1203732
 
9.7%
1 877046
 
7.0%
3 700158
 
5.6%
7 662367
 
5.3%
A 601866
 
4.8%
M 601866
 
4.8%
Other values (5) 288603
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12472693
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3069956
24.6%
2 2059635
16.5%
/ 1203732
 
9.7%
1203732
 
9.7%
: 1203732
 
9.7%
1 877046
 
7.0%
3 700158
 
5.6%
7 662367
 
5.3%
A 601866
 
4.8%
M 601866
 
4.8%
Other values (5) 288603
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12472693
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3069956
24.6%
2 2059635
16.5%
/ 1203732
 
9.7%
1203732
 
9.7%
: 1203732
 
9.7%
1 877046
 
7.0%
3 700158
 
5.6%
7 662367
 
5.3%
A 601866
 
4.8%
M 601866
 
4.8%
Other values (5) 288603
 
2.3%
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
WN
126469 
DL
89751 
AA
83013 
UA
65002 
OO
57287 
Other values (10)
180344 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1203732
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 126469
21.0%
DL 89751
14.9%
AA 83013
13.8%
UA 65002
10.8%
OO 57287
9.5%
YX 25385
 
4.2%
B6 22853
 
3.8%
AS 22743
 
3.8%
NK 21187
 
3.5%
MQ 19526
 
3.2%
Other values (5) 68650
11.4%

Length

2024-03-30T03:07:15.020985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 126469
21.0%
dl 89751
14.9%
aa 83013
13.8%
ua 65002
10.8%
oo 57287
9.5%
yx 25385
 
4.2%
b6 22853
 
3.8%
as 22743
 
3.8%
nk 21187
 
3.5%
mq 19526
 
3.2%
Other values (5) 68650
11.4%

Most occurring characters

ValueCountFrequency (%)
A 260856
21.7%
N 147656
12.3%
O 131514
10.9%
W 126469
10.5%
D 89751
 
7.5%
L 89751
 
7.5%
U 65002
 
5.4%
9 32444
 
2.7%
Y 25385
 
2.1%
X 25385
 
2.1%
Other values (11) 209519
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1203732
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 260856
21.7%
N 147656
12.3%
O 131514
10.9%
W 126469
10.5%
D 89751
 
7.5%
L 89751
 
7.5%
U 65002
 
5.4%
9 32444
 
2.7%
Y 25385
 
2.1%
X 25385
 
2.1%
Other values (11) 209519
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1203732
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 260856
21.7%
N 147656
12.3%
O 131514
10.9%
W 126469
10.5%
D 89751
 
7.5%
L 89751
 
7.5%
U 65002
 
5.4%
9 32444
 
2.7%
Y 25385
 
2.1%
X 25385
 
2.1%
Other values (11) 209519
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1203732
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 260856
21.7%
N 147656
12.3%
O 131514
10.9%
W 126469
10.5%
D 89751
 
7.5%
L 89751
 
7.5%
U 65002
 
5.4%
9 32444
 
2.7%
Y 25385
 
2.1%
X 25385
 
2.1%
Other values (11) 209519
17.4%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5923
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2309.749
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:15.326769image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile278
Q11037
median2079
Q33347
95-th percentile5380
Maximum8819
Range8818
Interquartile range (IQR)2310

Descriptive statistics

Standard deviation1567.6158
Coefficient of variation (CV)0.67869531
Kurtosis-0.59900956
Mean2309.749
Median Absolute Deviation (MAD)1118
Skewness0.59042832
Sum1.3901594 × 109
Variance2457419.4
MonotonicityNot monotonic
2024-03-30T03:07:15.818648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1150 336
 
0.1%
358 328
 
0.1%
312 308
 
0.1%
323 307
 
0.1%
374 302
 
0.1%
777 301
 
0.1%
555 299
 
< 0.1%
573 298
 
< 0.1%
512 287
 
< 0.1%
354 283
 
< 0.1%
Other values (5913) 598817
99.5%
ValueCountFrequency (%)
1 198
< 0.1%
2 187
< 0.1%
3 133
< 0.1%
4 168
< 0.1%
5 106
< 0.1%
6 89
< 0.1%
7 177
< 0.1%
8 121
< 0.1%
9 173
< 0.1%
10 195
< 0.1%
ValueCountFrequency (%)
8819 1
 
< 0.1%
8817 1
 
< 0.1%
8810 1
 
< 0.1%
8801 1
 
< 0.1%
8800 1
 
< 0.1%
8799 1
 
< 0.1%
8788 1
 
< 0.1%
8786 1
 
< 0.1%
8785 1
 
< 0.1%
8784 4
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12646.706
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:16.175390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1535.8975
Coefficient of variation (CV)0.12144644
Kurtosis-1.3043379
Mean12646.706
Median Absolute Deviation (MAD)1591
Skewness0.10825307
Sum7.6116226 × 109
Variance2358981
MonotonicityNot monotonic
2024-03-30T03:07:16.547252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 29919
 
5.0%
11298 26010
 
4.3%
11292 25684
 
4.3%
13930 22796
 
3.8%
12892 17523
 
2.9%
11057 16976
 
2.8%
12889 15879
 
2.6%
14747 15666
 
2.6%
14107 14037
 
2.3%
13204 13770
 
2.3%
Other values (326) 403606
67.1%
ValueCountFrequency (%)
10135 422
 
0.1%
10136 119
 
< 0.1%
10140 2044
0.3%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 436
 
0.1%
10155 92
 
< 0.1%
10157 145
 
< 0.1%
10158 213
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 142
 
< 0.1%
16218 146
 
< 0.1%
15991 62
 
< 0.1%
15919 1048
0.2%
15897 64
 
< 0.1%
15841 62
 
< 0.1%
15624 1099
0.2%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 53
 
< 0.1%

ORIGIN
Text

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:17.263283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1805598
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCLT
2nd rowJFK
3rd rowSYR
4th rowBGR
5th rowLGA
ValueCountFrequency (%)
atl 29919
 
5.0%
dfw 26010
 
4.3%
den 25684
 
4.3%
ord 22796
 
3.8%
lax 17523
 
2.9%
clt 16976
 
2.8%
las 15879
 
2.6%
sea 15666
 
2.6%
phx 14037
 
2.3%
mco 13770
 
2.3%
Other values (326) 403606
67.1%
2024-03-30T03:07:18.302357image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 206051
 
11.4%
L 164641
 
9.1%
S 154663
 
8.6%
D 142889
 
7.9%
T 95535
 
5.3%
O 92161
 
5.1%
C 91838
 
5.1%
M 81874
 
4.5%
F 75314
 
4.2%
W 71042
 
3.9%
Other values (16) 629590
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 206051
 
11.4%
L 164641
 
9.1%
S 154663
 
8.6%
D 142889
 
7.9%
T 95535
 
5.3%
O 92161
 
5.1%
C 91838
 
5.1%
M 81874
 
4.5%
F 75314
 
4.2%
W 71042
 
3.9%
Other values (16) 629590
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 206051
 
11.4%
L 164641
 
9.1%
S 154663
 
8.6%
D 142889
 
7.9%
T 95535
 
5.3%
O 92161
 
5.1%
C 91838
 
5.1%
M 81874
 
4.5%
F 75314
 
4.2%
W 71042
 
3.9%
Other values (16) 629590
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 206051
 
11.4%
L 164641
 
9.1%
S 154663
 
8.6%
D 142889
 
7.9%
T 95535
 
5.3%
O 92161
 
5.1%
C 91838
 
5.1%
M 81874
 
4.5%
F 75314
 
4.2%
W 71042
 
3.9%
Other values (16) 629590
34.9%
Distinct330
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:18.862447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.055089
Min length8

Characters and Unicode

Total characters7857414
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCharlotte, NC
2nd rowNew York, NY
3rd rowSyracuse, NY
4th rowBangor, ME
5th rowNew York, NY
ValueCountFrequency (%)
ca 65123
 
4.7%
tx 64035
 
4.6%
fl 50480
 
3.6%
san 32137
 
2.3%
ga 32108
 
2.3%
il 31893
 
2.3%
ny 31272
 
2.2%
chicago 30738
 
2.2%
atlanta 29919
 
2.1%
new 28622
 
2.0%
Other values (401) 1003804
71.7%
2024-03-30T03:07:19.718307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
798265
 
10.2%
a 602550
 
7.7%
, 601866
 
7.7%
o 431943
 
5.5%
e 413987
 
5.3%
n 386586
 
4.9%
t 378533
 
4.8%
l 351400
 
4.5%
i 299598
 
3.8%
r 282936
 
3.6%
Other values (47) 3309750
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7857414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
798265
 
10.2%
a 602550
 
7.7%
, 601866
 
7.7%
o 431943
 
5.5%
e 413987
 
5.3%
n 386586
 
4.9%
t 378533
 
4.8%
l 351400
 
4.5%
i 299598
 
3.8%
r 282936
 
3.6%
Other values (47) 3309750
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7857414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
798265
 
10.2%
a 602550
 
7.7%
, 601866
 
7.7%
o 431943
 
5.5%
e 413987
 
5.3%
n 386586
 
4.9%
t 378533
 
4.8%
l 351400
 
4.5%
i 299598
 
3.8%
r 282936
 
3.6%
Other values (47) 3309750
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7857414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
798265
 
10.2%
a 602550
 
7.7%
, 601866
 
7.7%
o 431943
 
5.5%
e 413987
 
5.3%
n 386586
 
4.9%
t 378533
 
4.8%
l 351400
 
4.5%
i 299598
 
3.8%
r 282936
 
3.6%
Other values (47) 3309750
42.1%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:20.186427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1731332
Min length4

Characters and Unicode

Total characters4919131
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNorth Carolina
2nd rowNew York
3rd rowNew York
4th rowMaine
5th rowNew York
ValueCountFrequency (%)
california 65123
 
9.5%
texas 64035
 
9.3%
florida 50480
 
7.3%
new 46208
 
6.7%
georgia 32108
 
4.7%
illinois 31893
 
4.6%
carolina 31582
 
4.6%
york 31272
 
4.5%
colorado 28486
 
4.1%
north 26955
 
3.9%
Other values (51) 280852
40.8%
2024-03-30T03:07:20.886439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 662442
13.5%
i 552226
 
11.2%
o 471161
 
9.6%
n 367257
 
7.5%
r 351547
 
7.1%
e 300267
 
6.1%
s 284743
 
5.8%
l 273195
 
5.6%
C 126966
 
2.6%
t 120110
 
2.4%
Other values (37) 1409217
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4919131
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 662442
13.5%
i 552226
 
11.2%
o 471161
 
9.6%
n 367257
 
7.5%
r 351547
 
7.1%
e 300267
 
6.1%
s 284743
 
5.8%
l 273195
 
5.6%
C 126966
 
2.6%
t 120110
 
2.4%
Other values (37) 1409217
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4919131
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 662442
13.5%
i 552226
 
11.2%
o 471161
 
9.6%
n 367257
 
7.5%
r 351547
 
7.1%
e 300267
 
6.1%
s 284743
 
5.8%
l 273195
 
5.6%
C 126966
 
2.6%
t 120110
 
2.4%
Other values (37) 1409217
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4919131
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 662442
13.5%
i 552226
 
11.2%
o 471161
 
9.6%
n 367257
 
7.5%
r 351547
 
7.1%
e 300267
 
6.1%
s 284743
 
5.8%
l 273195
 
5.6%
C 126966
 
2.6%
t 120110
 
2.4%
Other values (37) 1409217
28.6%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.569446
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:21.229680image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.901122
Coefficient of variation (CV)0.49297041
Kurtosis-1.3034784
Mean54.569446
Median Absolute Deviation (MAD)23
Skewness-0.041310785
Sum32843494
Variance723.67038
MonotonicityNot monotonic
2024-03-30T03:07:21.510508image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 65123
 
10.8%
74 64035
 
10.6%
33 50480
 
8.4%
34 32108
 
5.3%
41 31893
 
5.3%
22 31272
 
5.2%
82 28486
 
4.7%
36 25534
 
4.2%
38 20312
 
3.4%
93 17896
 
3.0%
Other values (42) 234727
39.0%
ValueCountFrequency (%)
1 4083
 
0.7%
2 11827
2.0%
3 3635
 
0.6%
4 518
 
0.1%
5 124
 
< 0.1%
11 1775
 
0.3%
12 1738
 
0.3%
13 13090
2.2%
14 628
 
0.1%
15 1207
 
0.2%
ValueCountFrequency (%)
93 17896
 
3.0%
92 6840
 
1.1%
91 65123
10.8%
88 967
 
0.2%
87 10166
 
1.7%
86 2260
 
0.4%
85 17741
 
2.9%
84 2815
 
0.5%
83 2388
 
0.4%
82 28486
4.7%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12646.664
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:21.796612image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1535.9055
Coefficient of variation (CV)0.12144748
Kurtosis-1.3043528
Mean12646.664
Median Absolute Deviation (MAD)1591
Skewness0.10831242
Sum7.6115971 × 109
Variance2359005.8
MonotonicityNot monotonic
2024-03-30T03:07:22.168536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 29915
 
5.0%
11298 26009
 
4.3%
11292 25681
 
4.3%
13930 22788
 
3.8%
12892 17518
 
2.9%
11057 16981
 
2.8%
12889 15880
 
2.6%
14747 15672
 
2.6%
14107 14032
 
2.3%
13204 13772
 
2.3%
Other values (326) 403618
67.1%
ValueCountFrequency (%)
10135 422
 
0.1%
10136 119
 
< 0.1%
10140 2043
0.3%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 436
 
0.1%
10155 92
 
< 0.1%
10157 145
 
< 0.1%
10158 213
 
< 0.1%
10165 9
 
< 0.1%
ValueCountFrequency (%)
16869 142
 
< 0.1%
16218 146
 
< 0.1%
15991 62
 
< 0.1%
15919 1048
0.2%
15897 64
 
< 0.1%
15841 62
 
< 0.1%
15624 1098
0.2%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 53
 
< 0.1%

DEST
Text

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:22.772498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1805598
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJFK
2nd rowCLT
3rd rowDTW
4th rowLGA
5th rowBGR
ValueCountFrequency (%)
atl 29915
 
5.0%
dfw 26009
 
4.3%
den 25681
 
4.3%
ord 22788
 
3.8%
lax 17518
 
2.9%
clt 16981
 
2.8%
las 15880
 
2.6%
sea 15672
 
2.6%
phx 14032
 
2.3%
mco 13772
 
2.3%
Other values (326) 403618
67.1%
2024-03-30T03:07:23.646255image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 206053
 
11.4%
L 164643
 
9.1%
S 154673
 
8.6%
D 142874
 
7.9%
T 95543
 
5.3%
O 92158
 
5.1%
C 91854
 
5.1%
M 81878
 
4.5%
F 75303
 
4.2%
W 71037
 
3.9%
Other values (16) 629582
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 206053
 
11.4%
L 164643
 
9.1%
S 154673
 
8.6%
D 142874
 
7.9%
T 95543
 
5.3%
O 92158
 
5.1%
C 91854
 
5.1%
M 81878
 
4.5%
F 75303
 
4.2%
W 71037
 
3.9%
Other values (16) 629582
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 206053
 
11.4%
L 164643
 
9.1%
S 154673
 
8.6%
D 142874
 
7.9%
T 95543
 
5.3%
O 92158
 
5.1%
C 91854
 
5.1%
M 81878
 
4.5%
F 75303
 
4.2%
W 71037
 
3.9%
Other values (16) 629582
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 206053
 
11.4%
L 164643
 
9.1%
S 154673
 
8.6%
D 142874
 
7.9%
T 95543
 
5.3%
O 92158
 
5.1%
C 91854
 
5.1%
M 81878
 
4.5%
F 75303
 
4.2%
W 71037
 
3.9%
Other values (16) 629582
34.9%
Distinct330
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:24.189928image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.054918
Min length8

Characters and Unicode

Total characters7857311
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowCharlotte, NC
3rd rowDetroit, MI
4th rowNew York, NY
5th rowBangor, ME
ValueCountFrequency (%)
ca 65121
 
4.7%
tx 64030
 
4.6%
fl 50480
 
3.6%
san 32129
 
2.3%
ga 32103
 
2.3%
il 31881
 
2.3%
ny 31276
 
2.2%
chicago 30726
 
2.2%
atlanta 29915
 
2.1%
new 28624
 
2.0%
Other values (401) 1003830
71.7%
2024-03-30T03:07:25.018771image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
798249
 
10.2%
a 602548
 
7.7%
, 601866
 
7.7%
o 431941
 
5.5%
e 413987
 
5.3%
n 386583
 
4.9%
t 378537
 
4.8%
l 351422
 
4.5%
i 299565
 
3.8%
r 282937
 
3.6%
Other values (47) 3309676
42.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7857311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
798249
 
10.2%
a 602548
 
7.7%
, 601866
 
7.7%
o 431941
 
5.5%
e 413987
 
5.3%
n 386583
 
4.9%
t 378537
 
4.8%
l 351422
 
4.5%
i 299565
 
3.8%
r 282937
 
3.6%
Other values (47) 3309676
42.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7857311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
798249
 
10.2%
a 602548
 
7.7%
, 601866
 
7.7%
o 431941
 
5.5%
e 413987
 
5.3%
n 386583
 
4.9%
t 378537
 
4.8%
l 351422
 
4.5%
i 299565
 
3.8%
r 282937
 
3.6%
Other values (47) 3309676
42.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7857311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
798249
 
10.2%
a 602548
 
7.7%
, 601866
 
7.7%
o 431941
 
5.5%
e 413987
 
5.3%
n 386583
 
4.9%
t 378537
 
4.8%
l 351422
 
4.5%
i 299565
 
3.8%
r 282937
 
3.6%
Other values (47) 3309676
42.1%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:25.399125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1732512
Min length4

Characters and Unicode

Total characters4919202
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowNorth Carolina
3rd rowMichigan
4th rowNew York
5th rowMaine
ValueCountFrequency (%)
california 65121
 
9.5%
texas 64030
 
9.3%
florida 50480
 
7.3%
new 46216
 
6.7%
georgia 32103
 
4.7%
illinois 31881
 
4.6%
carolina 31589
 
4.6%
york 31276
 
4.5%
colorado 28484
 
4.1%
north 26961
 
3.9%
Other values (51) 280865
40.8%
2024-03-30T03:07:26.185084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 662463
13.5%
i 552213
 
11.2%
o 471160
 
9.6%
n 367267
 
7.5%
r 351553
 
7.1%
e 300274
 
6.1%
s 284743
 
5.8%
l 273173
 
5.6%
C 126970
 
2.6%
t 120126
 
2.4%
Other values (37) 1409260
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4919202
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 662463
13.5%
i 552213
 
11.2%
o 471160
 
9.6%
n 367267
 
7.5%
r 351553
 
7.1%
e 300274
 
6.1%
s 284743
 
5.8%
l 273173
 
5.6%
C 126970
 
2.6%
t 120126
 
2.4%
Other values (37) 1409260
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4919202
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 662463
13.5%
i 552213
 
11.2%
o 471160
 
9.6%
n 367267
 
7.5%
r 351553
 
7.1%
e 300274
 
6.1%
s 284743
 
5.8%
l 273173
 
5.6%
C 126970
 
2.6%
t 120126
 
2.4%
Other values (37) 1409260
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4919202
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 662463
13.5%
i 552213
 
11.2%
o 471160
 
9.6%
n 367267
 
7.5%
r 351553
 
7.1%
e 300274
 
6.1%
s 284743
 
5.8%
l 273173
 
5.6%
C 126970
 
2.6%
t 120126
 
2.4%
Other values (37) 1409260
28.6%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.568961
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:26.524362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median45
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.90211
Coefficient of variation (CV)0.49299289
Kurtosis-1.3034989
Mean54.568961
Median Absolute Deviation (MAD)23
Skewness-0.041328217
Sum32843202
Variance723.7235
MonotonicityNot monotonic
2024-03-30T03:07:26.807753image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 65121
 
10.8%
74 64030
 
10.6%
33 50480
 
8.4%
34 32103
 
5.3%
41 31881
 
5.3%
22 31276
 
5.2%
82 28484
 
4.7%
36 25540
 
4.2%
38 20312
 
3.4%
93 17903
 
3.0%
Other values (42) 234736
39.0%
ValueCountFrequency (%)
1 4086
 
0.7%
2 11830
2.0%
3 3634
 
0.6%
4 518
 
0.1%
5 124
 
< 0.1%
11 1776
 
0.3%
12 1739
 
0.3%
13 13091
2.2%
14 629
 
0.1%
15 1206
 
0.2%
ValueCountFrequency (%)
93 17903
 
3.0%
92 6843
 
1.1%
91 65121
10.8%
88 966
 
0.2%
87 10164
 
1.7%
86 2259
 
0.4%
85 17745
 
2.9%
84 2816
 
0.5%
83 2388
 
0.4%
82 28484
4.7%

CRS_DEP_TIME
Real number (ℝ)

Distinct1230
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1338.6363
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:27.048235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile600
Q1909
median1327
Q31750
95-th percentile2145
Maximum2359
Range2358
Interquartile range (IQR)841

Descriptive statistics

Standard deviation504.09622
Coefficient of variation (CV)0.37657445
Kurtosis-1.082692
Mean1338.6363
Median Absolute Deviation (MAD)422
Skewness0.077685834
Sum8.0567966 × 108
Variance254113
MonotonicityNot monotonic
2024-03-30T03:07:27.273461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12248
 
2.0%
700 8766
 
1.5%
800 5227
 
0.9%
630 3786
 
0.6%
615 3621
 
0.6%
900 3597
 
0.6%
1000 3400
 
0.6%
730 3343
 
0.6%
830 3062
 
0.5%
715 2906
 
0.5%
Other values (1220) 551910
91.7%
ValueCountFrequency (%)
1 15
 
< 0.1%
4 1
 
< 0.1%
5 2
 
< 0.1%
8 27
< 0.1%
9 2
 
< 0.1%
10 38
< 0.1%
11 4
 
< 0.1%
12 4
 
< 0.1%
14 7
 
< 0.1%
15 66
< 0.1%
ValueCountFrequency (%)
2359 1037
0.2%
2358 77
 
< 0.1%
2357 122
 
< 0.1%
2356 58
 
< 0.1%
2355 232
 
< 0.1%
2354 60
 
< 0.1%
2353 84
 
< 0.1%
2352 9
 
< 0.1%
2351 4
 
< 0.1%
2350 137
 
< 0.1%

DEP_TIME
Real number (ℝ)

MISSING 

Distinct1426
Distinct (%)0.2%
Missing13965
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1338.2936
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:27.515185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile556
Q1905
median1326
Q31801
95-th percentile2207
Maximum2400
Range2399
Interquartile range (IQR)896

Descriptive statistics

Standard deviation530.07131
Coefficient of variation (CV)0.39607998
Kurtosis-0.95147354
Mean1338.2936
Median Absolute Deviation (MAD)431
Skewness0.009751456
Sum7.8678416 × 108
Variance280975.59
MonotonicityNot monotonic
2024-03-30T03:07:27.757918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
557 1452
 
0.2%
555 1400
 
0.2%
558 1392
 
0.2%
556 1364
 
0.2%
559 1277
 
0.2%
554 1204
 
0.2%
600 1171
 
0.2%
655 1141
 
0.2%
657 1108
 
0.2%
656 1020
 
0.2%
Other values (1416) 575372
95.6%
(Missing) 13965
 
2.3%
ValueCountFrequency (%)
1 146
< 0.1%
2 120
< 0.1%
3 120
< 0.1%
4 107
< 0.1%
5 112
< 0.1%
6 96
< 0.1%
7 110
< 0.1%
8 99
< 0.1%
9 95
< 0.1%
10 122
< 0.1%
ValueCountFrequency (%)
2400 106
< 0.1%
2359 137
< 0.1%
2358 149
< 0.1%
2357 164
< 0.1%
2356 154
< 0.1%
2355 163
< 0.1%
2354 178
< 0.1%
2353 193
< 0.1%
2352 167
< 0.1%
2351 160
< 0.1%

DEP_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1328
Distinct (%)0.2%
Missing13979
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean21.250342
Minimum-46
Maximum3343
Zeros27047
Zeros (%)4.5%
Negative282657
Negative (%)47.0%
Memory size4.6 MiB
2024-03-30T03:07:28.036601image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-46
5-th percentile-9
Q1-4
median0
Q320
95-th percentile115
Maximum3343
Range3389
Interquartile range (IQR)24

Descriptive statistics

Standard deviation70.484318
Coefficient of variation (CV)3.3168557
Kurtosis167.54493
Mean21.250342
Median Absolute Deviation (MAD)7
Skewness9.6895694
Sum12492800
Variance4968.0391
MonotonicityNot monotonic
2024-03-30T03:07:28.306580image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 36275
 
6.0%
-3 36157
 
6.0%
-4 35930
 
6.0%
-2 34006
 
5.7%
-1 31035
 
5.2%
-6 28676
 
4.8%
0 27047
 
4.5%
-7 23288
 
3.9%
-8 17929
 
3.0%
1 13189
 
2.2%
Other values (1318) 304355
50.6%
(Missing) 13979
 
2.3%
ValueCountFrequency (%)
-46 1
 
< 0.1%
-41 2
 
< 0.1%
-40 1
 
< 0.1%
-38 1
 
< 0.1%
-35 1
 
< 0.1%
-33 2
 
< 0.1%
-30 4
 
< 0.1%
-29 11
< 0.1%
-28 10
< 0.1%
-27 15
< 0.1%
ValueCountFrequency (%)
3343 1
< 0.1%
2989 1
< 0.1%
2905 1
< 0.1%
2900 1
< 0.1%
2871 1
< 0.1%
2795 1
< 0.1%
2765 1
< 0.1%
2445 2
< 0.1%
2391 1
< 0.1%
2341 1
< 0.1%

TAXI_OUT
Real number (ℝ)

MISSING 

Distinct172
Distinct (%)< 0.1%
Missing14563
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean17.521654
Minimum1
Maximum178
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:28.559503image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile34
Maximum178
Range177
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.26939
Coefficient of variation (CV)0.58609707
Kurtosis27.621035
Mean17.521654
Median Absolute Deviation (MAD)4
Skewness3.9821154
Sum10290520
Variance105.46037
MonotonicityNot monotonic
2024-03-30T03:07:28.809936image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 48419
 
8.0%
12 48208
 
8.0%
14 45081
 
7.5%
11 44848
 
7.5%
15 40668
 
6.8%
10 36875
 
6.1%
16 35737
 
5.9%
17 31098
 
5.2%
18 26822
 
4.5%
9 25336
 
4.2%
Other values (162) 204211
33.9%
ValueCountFrequency (%)
1 9
 
< 0.1%
2 19
 
< 0.1%
3 51
 
< 0.1%
4 207
 
< 0.1%
5 604
 
0.1%
6 2840
 
0.5%
7 7660
 
1.3%
8 15291
2.5%
9 25336
4.2%
10 36875
6.1%
ValueCountFrequency (%)
178 1
 
< 0.1%
173 1
 
< 0.1%
171 4
< 0.1%
170 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
167 2
 
< 0.1%
165 1
 
< 0.1%
164 2
 
< 0.1%
163 7
< 0.1%

TAXI_IN
Real number (ℝ)

MISSING 

Distinct179
Distinct (%)< 0.1%
Missing14764
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean8.4923812
Minimum1
Maximum236
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:29.087274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q15
median6
Q310
95-th percentile20
Maximum236
Range235
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.6301033
Coefficient of variation (CV)0.89846453
Kurtosis61.176732
Mean8.4923812
Median Absolute Deviation (MAD)2
Skewness5.5992189
Sum4985894
Variance58.218476
MonotonicityNot monotonic
2024-03-30T03:07:29.347215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 85024
14.1%
4 84741
14.1%
6 70964
11.8%
7 57000
9.5%
3 47244
7.8%
8 43137
7.2%
9 33610
 
5.6%
10 26781
 
4.4%
11 21268
 
3.5%
12 16496
 
2.7%
Other values (169) 100837
16.8%
(Missing) 14764
 
2.5%
ValueCountFrequency (%)
1 783
 
0.1%
2 11037
 
1.8%
3 47244
7.8%
4 84741
14.1%
5 85024
14.1%
6 70964
11.8%
7 57000
9.5%
8 43137
7.2%
9 33610
 
5.6%
10 26781
 
4.4%
ValueCountFrequency (%)
236 1
< 0.1%
233 1
< 0.1%
221 1
< 0.1%
198 1
< 0.1%
197 1
< 0.1%
196 1
< 0.1%
190 1
< 0.1%
189 1
< 0.1%
188 2
< 0.1%
185 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1306
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1476.4951
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:29.601257image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile656
Q11048
median1509
Q31926
95-th percentile2301
Maximum2359
Range2358
Interquartile range (IQR)878

Descriptive statistics

Standard deviation539.00035
Coefficient of variation (CV)0.36505394
Kurtosis-0.47111061
Mean1476.4951
Median Absolute Deviation (MAD)427
Skewness-0.31967633
Sum8.886522 × 108
Variance290521.38
MonotonicityNot monotonic
2024-03-30T03:07:29.997707image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3051
 
0.5%
1900 1910
 
0.3%
2100 1875
 
0.3%
1810 1775
 
0.3%
1855 1773
 
0.3%
1915 1748
 
0.3%
2145 1736
 
0.3%
2000 1730
 
0.3%
1140 1706
 
0.3%
905 1672
 
0.3%
Other values (1296) 582890
96.8%
ValueCountFrequency (%)
1 79
 
< 0.1%
2 161
 
< 0.1%
3 282
 
< 0.1%
4 155
 
< 0.1%
5 755
0.1%
6 77
 
< 0.1%
7 56
 
< 0.1%
8 75
 
< 0.1%
9 128
 
< 0.1%
10 601
0.1%
ValueCountFrequency (%)
2359 3051
0.5%
2358 819
 
0.1%
2357 713
 
0.1%
2356 564
 
0.1%
2355 1289
0.2%
2354 508
 
0.1%
2353 564
 
0.1%
2352 600
 
0.1%
2351 287
 
< 0.1%
2350 958
 
0.2%

ARR_TIME
Real number (ℝ)

MISSING 

Distinct1440
Distinct (%)0.2%
Missing14764
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean1427.2859
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:30.291054image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile259
Q11018
median1441
Q31916
95-th percentile2257
Maximum2400
Range2399
Interquartile range (IQR)898

Descriptive statistics

Standard deviation579.08175
Coefficient of variation (CV)0.40572232
Kurtosis-0.42709795
Mean1427.2859
Median Absolute Deviation (MAD)440
Skewness-0.3953112
Sum8.379624 × 108
Variance335335.67
MonotonicityNot monotonic
2024-03-30T03:07:30.540436image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1136 673
 
0.1%
2132 652
 
0.1%
1146 636
 
0.1%
1121 634
 
0.1%
1645 631
 
0.1%
1138 630
 
0.1%
1135 628
 
0.1%
1137 627
 
0.1%
1227 626
 
0.1%
1856 624
 
0.1%
Other values (1430) 580741
96.5%
(Missing) 14764
 
2.5%
ValueCountFrequency (%)
1 457
0.1%
2 453
0.1%
3 406
0.1%
4 411
0.1%
5 409
0.1%
6 411
0.1%
7 397
0.1%
8 420
0.1%
9 351
0.1%
10 371
0.1%
ValueCountFrequency (%)
2400 386
0.1%
2359 404
0.1%
2358 438
0.1%
2357 466
0.1%
2356 491
0.1%
2355 444
0.1%
2354 499
0.1%
2353 472
0.1%
2352 448
0.1%
2351 519
0.1%

ARR_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1358
Distinct (%)0.2%
Missing16808
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean16.641825
Minimum-86
Maximum3337
Zeros10416
Zeros (%)1.7%
Negative312657
Negative (%)51.9%
Memory size4.6 MiB
2024-03-30T03:07:30.787648image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile-24
Q1-13
median-2
Q320
95-th percentile116
Maximum3337
Range3423
Interquartile range (IQR)33

Descriptive statistics

Standard deviation72.101824
Coefficient of variation (CV)4.3325671
Kurtosis152.87673
Mean16.641825
Median Absolute Deviation (MAD)13
Skewness9.0734374
Sum9736433
Variance5198.673
MonotonicityNot monotonic
2024-03-30T03:07:31.064913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-10 15439
 
2.6%
-11 15407
 
2.6%
-9 15156
 
2.5%
-12 15027
 
2.5%
-8 14946
 
2.5%
-7 14693
 
2.4%
-13 14628
 
2.4%
-6 14129
 
2.3%
-14 14046
 
2.3%
-15 13315
 
2.2%
Other values (1348) 438272
72.8%
(Missing) 16808
 
2.8%
ValueCountFrequency (%)
-86 1
 
< 0.1%
-85 1
 
< 0.1%
-74 1
 
< 0.1%
-73 2
< 0.1%
-72 1
 
< 0.1%
-71 1
 
< 0.1%
-69 1
 
< 0.1%
-68 2
< 0.1%
-66 3
< 0.1%
-65 2
< 0.1%
ValueCountFrequency (%)
3337 1
< 0.1%
2980 1
< 0.1%
2912 1
< 0.1%
2891 1
< 0.1%
2854 1
< 0.1%
2786 1
< 0.1%
2748 1
< 0.1%
2429 1
< 0.1%
2424 1
< 0.1%
2418 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0.0
587260 
1.0
 
14606

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1805598
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 587260
97.6%
1.0 14606
 
2.4%

Length

2024-03-30T03:07:31.302246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:07:31.471390image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 587260
97.6%
1.0 14606
 
2.4%

Most occurring characters

ValueCountFrequency (%)
0 1189126
65.9%
. 601866
33.3%
1 14606
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1189126
65.9%
. 601866
33.3%
1 14606
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1189126
65.9%
. 601866
33.3%
1 14606
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1189126
65.9%
. 601866
33.3%
1 14606
 
0.8%

CANCELLATION_CODE
Categorical

MISSING 

Distinct3
Distinct (%)< 0.1%
Missing587260
Missing (%)97.6%
Memory size4.6 MiB
B
6863 
A
4013 
C
3730 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14606
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowC
5th rowC

Common Values

ValueCountFrequency (%)
B 6863
 
1.1%
A 4013
 
0.7%
C 3730
 
0.6%
(Missing) 587260
97.6%

Length

2024-03-30T03:07:31.646600image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:07:31.829434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
b 6863
47.0%
a 4013
27.5%
c 3730
25.5%

Most occurring characters

ValueCountFrequency (%)
B 6863
47.0%
A 4013
27.5%
C 3730
25.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14606
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 6863
47.0%
A 4013
27.5%
C 3730
25.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14606
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 6863
47.0%
A 4013
27.5%
C 3730
25.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14606
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 6863
47.0%
A 4013
27.5%
C 3730
25.5%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0.0
599664 
1.0
 
2202

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1805598
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 599664
99.6%
1.0 2202
 
0.4%

Length

2024-03-30T03:07:32.067658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:07:32.289615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 599664
99.6%
1.0 2202
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1201530
66.5%
. 601866
33.3%
1 2202
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1201530
66.5%
. 601866
33.3%
1 2202
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1201530
66.5%
. 601866
33.3%
1 2202
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1805598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1201530
66.5%
. 601866
33.3%
1 2202
 
0.1%

AIR_TIME
Real number (ℝ)

MISSING 

Distinct602
Distinct (%)0.1%
Missing16808
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean115.15852
Minimum8
Maximum671
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:32.552101image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile35
Q163
median98
Q3145
95-th percentile272
Maximum671
Range663
Interquartile range (IQR)82

Descriptive statistics

Standard deviation70.952552
Coefficient of variation (CV)0.61612939
Kurtosis2.1090581
Mean115.15852
Median Absolute Deviation (MAD)39
Skewness1.3573678
Sum67374416
Variance5034.2646
MonotonicityNot monotonic
2024-03-30T03:07:32.900430image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 5037
 
0.8%
64 4865
 
0.8%
65 4819
 
0.8%
62 4803
 
0.8%
61 4782
 
0.8%
66 4747
 
0.8%
60 4736
 
0.8%
54 4715
 
0.8%
67 4677
 
0.8%
59 4672
 
0.8%
Other values (592) 537205
89.3%
(Missing) 16808
 
2.8%
ValueCountFrequency (%)
8 4
 
< 0.1%
9 18
 
< 0.1%
10 21
 
< 0.1%
11 9
 
< 0.1%
12 3
 
< 0.1%
13 22
 
< 0.1%
14 25
 
< 0.1%
15 41
 
< 0.1%
16 103
< 0.1%
17 234
< 0.1%
ValueCountFrequency (%)
671 1
 
< 0.1%
654 1
 
< 0.1%
653 1
 
< 0.1%
652 2
< 0.1%
651 1
 
< 0.1%
648 1
 
< 0.1%
647 2
< 0.1%
646 1
 
< 0.1%
641 1
 
< 0.1%
640 3
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct1058
Distinct (%)0.6%
Missing434786
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean26.42749
Minimum0
Maximum3337
Zeros70641
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:33.222031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q323
95-th percentile108
Maximum3337
Range3337
Interquartile range (IQR)23

Descriptive statistics

Standard deviation81.568453
Coefficient of variation (CV)3.0865002
Kurtosis177.96967
Mean26.42749
Median Absolute Deviation (MAD)5
Skewness10.560613
Sum4415505
Variance6653.4125
MonotonicityNot monotonic
2024-03-30T03:07:33.549519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 70641
 
11.7%
2 3291
 
0.5%
1 3217
 
0.5%
6 3082
 
0.5%
3 3027
 
0.5%
4 2981
 
0.5%
7 2876
 
0.5%
5 2867
 
0.5%
15 2689
 
0.4%
8 2599
 
0.4%
Other values (1048) 69810
 
11.6%
(Missing) 434786
72.2%
ValueCountFrequency (%)
0 70641
11.7%
1 3217
 
0.5%
2 3291
 
0.5%
3 3027
 
0.5%
4 2981
 
0.5%
5 2867
 
0.5%
6 3082
 
0.5%
7 2876
 
0.5%
8 2599
 
0.4%
9 2522
 
0.4%
ValueCountFrequency (%)
3337 1
< 0.1%
2980 1
< 0.1%
2891 1
< 0.1%
2693 1
< 0.1%
2635 1
< 0.1%
2429 1
< 0.1%
2388 1
< 0.1%
2328 1
< 0.1%
2279 1
< 0.1%
2275 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct477
Distinct (%)0.3%
Missing434786
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean4.5310749
Minimum0
Maximum1380
Zeros155236
Zeros (%)25.8%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:33.850843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile20
Maximum1380
Range1380
Interquartile range (IQR)0

Descriptive statistics

Standard deviation30.838416
Coefficient of variation (CV)6.8059824
Kurtosis507.10369
Mean4.5310749
Median Absolute Deviation (MAD)0
Skewness18.191266
Sum757052
Variance951.00792
MonotonicityNot monotonic
2024-03-30T03:07:34.340827image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 155236
 
25.8%
15 235
 
< 0.1%
20 210
 
< 0.1%
16 206
 
< 0.1%
18 196
 
< 0.1%
6 196
 
< 0.1%
17 196
 
< 0.1%
7 192
 
< 0.1%
10 188
 
< 0.1%
19 188
 
< 0.1%
Other values (467) 10037
 
1.7%
(Missing) 434786
72.2%
ValueCountFrequency (%)
0 155236
25.8%
1 172
 
< 0.1%
2 175
 
< 0.1%
3 172
 
< 0.1%
4 164
 
< 0.1%
5 162
 
< 0.1%
6 196
 
< 0.1%
7 192
 
< 0.1%
8 178
 
< 0.1%
9 158
 
< 0.1%
ValueCountFrequency (%)
1380 1
< 0.1%
1312 1
< 0.1%
1281 1
< 0.1%
1269 1
< 0.1%
1266 1
< 0.1%
1238 1
< 0.1%
1236 1
< 0.1%
1151 1
< 0.1%
1124 1
< 0.1%
1118 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct506
Distinct (%)0.3%
Missing434786
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean14.0079
Minimum0
Maximum1651
Zeros90483
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:34.661222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q316
95-th percentile64
Maximum1651
Range1651
Interquartile range (IQR)16

Descriptive statistics

Standard deviation37.36045
Coefficient of variation (CV)2.6670985
Kurtosis220.03042
Mean14.0079
Median Absolute Deviation (MAD)0
Skewness10.484565
Sum2340440
Variance1395.8033
MonotonicityNot monotonic
2024-03-30T03:07:34.999477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90483
 
15.0%
1 4205
 
0.7%
2 3061
 
0.5%
3 2973
 
0.5%
4 2688
 
0.4%
5 2550
 
0.4%
15 2397
 
0.4%
6 2351
 
0.4%
7 2205
 
0.4%
16 2121
 
0.4%
Other values (496) 52046
 
8.6%
(Missing) 434786
72.2%
ValueCountFrequency (%)
0 90483
15.0%
1 4205
 
0.7%
2 3061
 
0.5%
3 2973
 
0.5%
4 2688
 
0.4%
5 2550
 
0.4%
6 2351
 
0.4%
7 2205
 
0.4%
8 2039
 
0.3%
9 2025
 
0.3%
ValueCountFrequency (%)
1651 1
< 0.1%
1256 1
< 0.1%
1253 1
< 0.1%
1223 1
< 0.1%
1198 1
< 0.1%
1184 1
< 0.1%
1172 1
< 0.1%
1154 1
< 0.1%
1144 1
< 0.1%
1140 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct91
Distinct (%)0.1%
Missing434786
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean0.10955231
Minimum0
Maximum581
Zeros166287
Zeros (%)27.6%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:35.326708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum581
Range581
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6644116
Coefficient of variation (CV)24.320908
Kurtosis14777.571
Mean0.10955231
Median Absolute Deviation (MAD)0
Skewness86.503517
Sum18304
Variance7.0990894
MonotonicityNot monotonic
2024-03-30T03:07:35.656576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 166287
 
27.6%
7 32
 
< 0.1%
13 31
 
< 0.1%
6 31
 
< 0.1%
18 29
 
< 0.1%
5 28
 
< 0.1%
4 26
 
< 0.1%
2 25
 
< 0.1%
10 25
 
< 0.1%
19 25
 
< 0.1%
Other values (81) 541
 
0.1%
(Missing) 434786
72.2%
ValueCountFrequency (%)
0 166287
27.6%
1 23
 
< 0.1%
2 25
 
< 0.1%
3 21
 
< 0.1%
4 26
 
< 0.1%
5 28
 
< 0.1%
6 31
 
< 0.1%
7 32
 
< 0.1%
8 23
 
< 0.1%
9 24
 
< 0.1%
ValueCountFrequency (%)
581 1
< 0.1%
241 1
< 0.1%
200 1
< 0.1%
156 1
< 0.1%
152 1
< 0.1%
139 1
< 0.1%
137 1
< 0.1%
135 2
< 0.1%
133 1
< 0.1%
128 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct871
Distinct (%)0.5%
Missing434786
Missing (%)72.2%
Infinite0
Infinite (%)0.0%
Mean33.519302
Minimum0
Maximum2557
Zeros72648
Zeros (%)12.1%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:07:36.022596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q340
95-th percentile141
Maximum2557
Range2557
Interquartile range (IQR)40

Descriptive statistics

Standard deviation70.855023
Coefficient of variation (CV)2.1138573
Kurtosis102.44716
Mean33.519302
Median Absolute Deviation (MAD)9
Skewness7.4326583
Sum5600405
Variance5020.4342
MonotonicityNot monotonic
2024-03-30T03:07:36.385759image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 72648
 
12.1%
15 2023
 
0.3%
16 1930
 
0.3%
17 1864
 
0.3%
18 1846
 
0.3%
19 1767
 
0.3%
20 1731
 
0.3%
21 1604
 
0.3%
22 1601
 
0.3%
14 1530
 
0.3%
Other values (861) 78536
 
13.0%
(Missing) 434786
72.2%
ValueCountFrequency (%)
0 72648
12.1%
1 1121
 
0.2%
2 1162
 
0.2%
3 1203
 
0.2%
4 1147
 
0.2%
5 1146
 
0.2%
6 1249
 
0.2%
7 1217
 
0.2%
8 1349
 
0.2%
9 1448
 
0.2%
ValueCountFrequency (%)
2557 1
< 0.1%
2032 1
< 0.1%
2011 1
< 0.1%
1919 1
< 0.1%
1833 1
< 0.1%
1745 1
< 0.1%
1669 1
< 0.1%
1661 1
< 0.1%
1627 1
< 0.1%
1623 1
< 0.1%

Interactions

2024-03-30T03:06:57.068682image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:50.059591image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:57.061565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:03.679899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:11.433653image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:18.059142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:25.322395image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:31.852862image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:38.559883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:44.498983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:51.166271image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:58.035084image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:04.678791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:11.546570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:18.218111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:25.526994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:35.507676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:41.574076image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:46.621493image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:51.324281image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:57.364639image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:50.632401image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:57.360572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:04.118160image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:11.786373image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:18.414520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:25.669693image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:32.225908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:38.895957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:44.762640image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:51.501774image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:58.396623image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:05.053189image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:11.869286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:18.514434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:26.046125image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:35.903985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:41.840120image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:46.960461image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:51.607097image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:57.617132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:50.958606image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:57.701951image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:04.518923image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:12.141022image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:18.747443image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:25.982558image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:32.570342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:39.216087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:45.096699image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:51.821969image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:58.703127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:05.423456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:12.244416image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:18.855129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:26.414965image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:36.308057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:42.143405image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:47.166724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:51.850917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:57.930717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:51.299861image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:58.057152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:04.953142image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:12.470980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:19.094934image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:26.368989image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:32.935011image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:39.572791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:45.469760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:52.208307image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:59.065513image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:05.916918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:12.595283image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:19.217829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:26.787926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:36.623925image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:42.426426image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:47.388490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:52.175175image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:58.222446image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:51.611431image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:58.405644image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:05.348329image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:12.801358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:19.403697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:26.824801image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:33.259932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:39.894789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:45.794697image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:52.517803image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:59.373055image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:06.307103image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:12.927013image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:19.584751image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:27.141167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:36.913727image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:42.657490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:47.588988image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:52.567400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:58.520798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:51.990130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:58.761211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:05.932672image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:13.146831image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:19.762121image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:27.153133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:33.597477image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:40.268442image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:46.179655image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:52.873180image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:59.725136image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:06.665290image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:13.275248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:20.024560image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:27.642009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:37.239409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:42.933717image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:47.806102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:52.837724image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:58.768621image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:52.337511image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:59.106935image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:06.617810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:13.453251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:20.190445image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:27.458569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:33.929880image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:40.578190image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:46.649706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:53.188915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:06:03.276490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:10.361206image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:17.021664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:24.274966image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:33.941502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:40.493167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:45.684224image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:50.286789image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:55.926205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:07:01.974236image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:56.114687image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:02.662576image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:10.480268image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:17.125472image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:24.349248image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:30.965274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:37.633464image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:43.701108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:50.272315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:57.090865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:03.582050image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:10.656908image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:17.290489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:24.572998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:34.393866image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:40.762937image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:45.916254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:50.551968image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:56.255287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:07:02.256716image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:56.403328image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:02.966688image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:10.767252image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:17.402500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:24.643163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:31.213638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:37.910124image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:43.944743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:50.522891image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:57.362709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:03.884743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:10.927846image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:17.536411image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:24.838004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:34.748066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:41.038135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:46.169388image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:50.793669image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:56.513201image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:07:02.531964image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:04:56.700843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:03.289582image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:11.062469image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:17.665354image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:24.936995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:31.484274image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:38.227408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:44.199916image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:50.797393image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:05:57.642482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:04.229550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:11.187996image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:17.810225image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:25.146844image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:35.127634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:41.313971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:46.402282image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:51.076315image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:06:56.801625image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T03:07:03.219775image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:07:06.416994image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
017/3/2023 12:00:00 AM9E490111057CLTCharlotte, NCNorth Carolina3612478JFKNew York, NYNew York2219422016.034.037.055.022002342.0102.00.0NaN0.0114.00.00.068.00.034.0
117/3/2023 12:00:00 AM9E490112478JFKNew York, NYNew York2211057CLTCharlotte, NCNorth Carolina3616261733.067.040.07.018501946.056.00.0NaN0.086.056.00.00.00.00.0
217/3/2023 12:00:00 AM9E490215096SYRSyracuse, NYNew York2211433DTWDetroit, MIMichigan43610958.0228.015.010.07391121.0222.00.0NaN0.058.0222.00.00.00.00.0
317/3/2023 12:00:00 AM9E490410581BGRBangor, MEMaine1212953LGANew York, NYNew York2212181215.0-3.07.07.013591332.0-27.00.0NaN0.063.0NaNNaNNaNNaNNaN
417/3/2023 12:00:00 AM9E490412953LGANew York, NYNew York2210581BGRBangor, MEMaine12945943.0-2.019.05.011331102.0-31.00.0NaN0.055.0NaNNaNNaNNaNNaN
517/3/2023 12:00:00 AM9E490512953LGANew York, NYNew York2211267DAYDayton, OHOhio4410301024.0-6.015.03.012391203.0-36.00.0NaN0.081.0NaNNaNNaNNaNNaN
617/3/2023 12:00:00 AM9E490610397ATLAtlanta, GAGeorgia3413377MLUMonroe, LALouisiana7210101005.0-5.022.03.010481036.0-12.00.0NaN0.066.0NaNNaNNaNNaNNaN
717/3/2023 12:00:00 AM9E490613377MLUMonroe, LALouisiana7210397ATLAtlanta, GAGeorgia3411331139.06.027.07.014191419.00.00.0NaN0.066.0NaNNaNNaNNaNNaN
817/3/2023 12:00:00 AM9E490710581BGRBangor, MEMaine1212478JFKNew York, NYNew York2219191910.0-9.011.017.021002046.0-14.00.0NaN0.068.0NaNNaNNaNNaNNaN
917/3/2023 12:00:00 AM9E491110397ATLAtlanta, GAGeorgia3411150CSGColumbus, GAGeorgia3412041206.02.011.04.012511241.0-10.00.0NaN0.020.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
60185677/30/2023 12:00:00 AMYX583212953LGANew York, NYNew York2212339INDIndianapolis, INIndiana4210511105.014.016.010.013181314.0-4.00.0NaN0.0103.0NaNNaNNaNNaNNaN
60185777/30/2023 12:00:00 AMYX583312478JFKNew York, NYNew York2211066CMHColumbus, OHOhio4421302125.0-5.029.012.023392327.0-12.00.0NaN0.081.0NaNNaNNaNNaNNaN
60185877/30/2023 12:00:00 AMYX583412953LGANew York, NYNew York2213485MSNMadison, WIWisconsin4520502144.054.020.04.022252311.046.00.0NaN0.0123.046.00.00.00.00.0
60185977/30/2023 12:00:00 AMYX583812339INDIndianapolis, INIndiana4210721BOSBoston, MAMassachusetts1317001659.0-1.014.020.019361918.0-18.00.0NaN0.0105.0NaNNaNNaNNaNNaN
60186077/30/2023 12:00:00 AMYX583910721BOSBoston, MAMassachusetts1311066CMHColumbus, OHOhio4413301326.0-4.054.05.015451607.022.00.0NaN0.0102.00.00.022.00.00.0
60186177/30/2023 12:00:00 AMYX584010721BOSBoston, MAMassachusetts1311042CLECleveland, OHOhio44800755.0-5.026.022.010151018.03.00.0NaN0.095.0NaNNaNNaNNaNNaN
60186277/30/2023 12:00:00 AMYX584011042CLECleveland, OHOhio4410721BOSBoston, MAMassachusetts1311001053.0-7.015.013.012481233.0-15.00.0NaN0.072.0NaNNaNNaNNaNNaN
60186377/30/2023 12:00:00 AMYX584412953LGANew York, NYNew York2210721BOSBoston, MAMassachusetts1310001026.026.021.010.011361133.0-3.00.0NaN0.036.0NaNNaNNaNNaNNaN
60186477/30/2023 12:00:00 AMYX584510154ACKNantucket, MAMassachusetts1312478JFKNew York, NYNew York2211521149.0-3.010.06.013091253.0-16.00.0NaN0.048.0NaNNaNNaNNaNNaN
60186577/30/2023 12:00:00 AMYX584611066CMHColumbus, OHOhio4412478JFKNew York, NYNew York2211451138.0-7.018.012.013591335.0-24.00.0NaN0.087.0NaNNaNNaNNaNNaN